Thin-Bed Reservoir Analysis from Borehole Electrical Images

Trouiller, J-C.; Delhomme, J.P.; Carlin, S.; Anxionnaz, H.

doi:10.2118/19578-ms

Cited by 16 publications

(3 citation statements)

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“…Borehole images have been used for thin-bed analysis for more than a decade (e.g., Trouiller et al 1989). Their unique high vertical resolution (generally less than 2 inches) allows identification and quantification of fine lithological laminations.…”

Section: Thin-bed Analysis (Borehole Images and Core)mentioning

confidence: 99%

A Fresh Look at Thin-bed Prospectivity Within Conventional Shallow Water Clastic Plays; Implication for Significant Bypassed pay

et al. 2014

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Thin-bedded sands in shallow water clastic deposits are widely neglected as exploration targets simply due to conventional wisdom that focuses more on the "charismatic" blocky clean sand packages. In addition, the lack of a strong, characteristic motif on the log response and complexity in quantitative characterization of these thinly laminated sequences leads to even less attention to such sequences as potential targets. This can have significant impact on the certainty of realistic reserve calculation in exploration and development planning in regions such as Southeast Asia where these thin-bedded sands are a considerable portion of the stratigraphic column.This study has two phases; in the first phase, a novel technique is presented to identify and quantify thin sand laminations using borehole images and core. Both borehole image and core data is converted to binary data representing shale and sand facies. Then these images are used to quantify the total thickness of thinly-laminated sands on the basis of specific cutoff values. In the second phase, a comprehensive analysis of a local present-day analogue determines the possible hydrocarbon storage capacity of thin-bedded deposits. This is accomplished through an area/volume approach. In this approach, after locating the pertinent locality within the depositional environment that can be considered as the equivalent to the subsurface section under study, the sand volume vs. area of thin-bedded heterolithic facies is computed. This is used to estimate the storage capacity of these facies at the field-scale. Additionally, the storage capacity is compared to the capacity of channelfill sands as conventional targets in the same setting.Results of this study show that the new technique using high-resolution borehole images can successfully identify and quantify thin-bedded sands, and results have an excellent match with core data. Also, the core results confirm that these deposits have sufficient reservoir quality (porosity and permeability), particularly for gas production. This work demonstrates that, contrary to conventional belief, the storage capacity of thin-bedded sands can be significantly high and sometimes may even exceed that of blocky channel or mouth-bar sands. This implies that substantial quantities of pay are continuously bypassed in basins where these sequences are expected.

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Section: Thin-bed Analysis (Borehole Images and Core)mentioning

confidence: 99%

A Fresh Look at Thin-bed Prospectivity Within Conventional Shallow Water Clastic Plays; Implication for Significant Bypassed pay

et al. 2014

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show abstract

“…The borehole electrical images provide a wealth of geologic data obtainable only from cores previously. Even though spatial resolution of features visible on the images (down to 0.2 inch) is lower than that on a core, borehole images can offer major geologic information where core is absent [1][2][3][4][5]. However, the generated image essentially provides numerical data in the form of log curves which may be useful to quantify the images and classify grain size of rocks [6].…”

Section: Introductionmentioning

confidence: 99%

A grain size auto-classification of Baikouquan Formation, Mahu Depression, Junggar Basin, China

et al. 2020

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The borehole electrical imaging log offers general visual borehole wall, leaving no doubt that the conductivity contrast is sufficient to obtain a qualitative gain-size distribution of rocks. In this study, an automatic grain-size classification method is proposed using gray values of borehole electrical images from Baikouquan Formation in Mahu Depression. The first stage is comparing electrical images with cores. Gravels, sands, silts and clays are all discovered in the cores. The gravels are “mottled” in electrical images, and the bigger the spots, the coarser the gravels. The images of sands are homogeneous bright colored, and the coarser the sandy grains, the brighter the images. The electrical images of silts and clays are homogeneous brown and dark-brown colored. The second stage is auto-discriminating four categories of grain sizes roughly using averages and variances of gray values. The variances of gray values of gravels are high, whereas those of sands are medium. The gray averages of silts are between 160 and 220, whereas those of clays are larger than 200. The third stage is auto-classifying three kinds of gravels or sands finely using frequency distribution of gray values. The gray values of frequency peaks of cobbles are less than 50 and frequencies are larger than 15%, whereas those of pebbles are less than 50 or larger than 200 and frequencies are between 10% and 20%; almost gray frequencies of granules are less than 10%. The dominated gray values of coarse sandstones, medium sandstones and fine sandstones are less than 50, between 50 and 160 and ranged from 160 to 240, respectively. The proposed method is demonstrated to be useful and fast to auto-classify grain size of various rocks in conglomeratic environments.

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“…The trivial outcome of interpreting such logs in the petrophysical workflows and using methods such as Waxman and Smits (1968), is a total water saturation (SW T ) log that overestimates the actual water saturation in the sand. A great part of thin bed evaluation therefore is to identify and characterize these types of reservoirs using enhanced tools for reading the horizontal and vertical resistivity (Trouiller et al 1989;Boay et al 2017;Jun and Zung 2018), utilizing advanced algorithms to enhance the resolution of the conventional logs and improve the hydrocarbon in-place estimates (Thomas and Stieber 1975;Zemanek 1987).…”

Section: Introductionmentioning

confidence: 99%

Designing a pragmatic solution for complex numerical modeling problem in thinly laminated reservoirs

Hajizadeh

Mudaliar

Tewari

2019

J Petrol Explor Prod Technol

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Unquestionably, water breakthrough into the producing wells is one of the biggest threats to the development and production of oil and gas reservoirs around the world. The predicted water production profile from a numerical simulation model directly affects the estimated recoverable volumes and hence the decision making process for the field development and the facility design to handle the produced water. The problem of reliably predicting the water production profile exacerbates when dealing with thinly laminated sand reservoirs. Calculating the total water saturation (SW T ) using well log data in the hydrocarbon zone in this type of reservoirs is adversely affected by the convoluted readings of the conventional logging instruments whenever the resolution of the laminations goes beyond the tool resolution (i.e. 0.5 feet). This in turn leads to unrealistic movement of water in the model, pre-mature water breakthrough, pessimistic forecast of oil and gas and overestimation of required water treatment system capacity. Although some advancement has been made in the identification of these types of reservoirs, their reservoir modeling still remains a challenge for petroleum industry due to limitations in developing proper methods to estimate petrophysical properties using conventional logs and also the computational cost of running extremely fine numerical models. In this study various issues and challenges of modeling these types of reservoirs are addressed. A workflow for numerical modeling of thinly laminated sands at affordable vertical grid resolutions has been proposed that properly uses the available geological, petrophysical and engineering data in an effective (vs. total porosity) system. The methodology of reproducing the volumetrics and capturing heterogeneity at various scales, through proper rock-typing and use of net to gross ratios are discussed in detail. The data requirement for modeling, reservoir management and performance monitoring of thin beds is also elucidated. The presented workflow provides a simple yet pragmatic tool that can help best represent the thinly laminated sands at affordable measurement and computation costs, mitigate the risks associated with thin bed reservoir development and increase the value of field development planning.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Thin-Bed Reservoir Analysis from Borehole Electrical Images

Cited by 16 publications

References 1 publication

A Fresh Look at Thin-bed Prospectivity Within Conventional Shallow Water Clastic Plays; Implication for Significant Bypassed pay

A Fresh Look at Thin-bed Prospectivity Within Conventional Shallow Water Clastic Plays; Implication for Significant Bypassed pay

A grain size auto-classification of Baikouquan Formation, Mahu Depression, Junggar Basin, China

Designing a pragmatic solution for complex numerical modeling problem in thinly laminated reservoirs

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